4.4 KiB
4.4 KiB
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Writing to Microsoft SQLServer¶
- Insure the Microsoft SQL Server is installed and you have access i.e account information
- The target database must be created before hand.
- We created an authentication file that will contain user account and location of the database
The cell below creates a dataframe that will be stored in a Microsoft SQL Server database.
NOTE This was not tested with a cloud instance
In [1]:
# # Writing to Google Bigquery database # import transport from transport import providers import pandas as pd import os AUTH_FOLDER = os.environ['DT_AUTH_FOLDER'] #-- location of the service key MSSQL_AUTH_FILE= os.sep.join([AUTH_FOLDER,'mssql.json']) _data = pd.DataFrame({"name":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]}) msw = transport.factory.instance(provider=providers.MSSQL,table='friends',context='write',auth_file=MSSQL_AUTH_FILE) msw.write(_data,if_exists='replace') #-- default is append print (['data transport version ', transport.__version__])
['data transport version ', '2.0.0']
Reading from Microsoft SQL Server database¶
The cell below reads the data that has been written by the cell above and computes the average age within an MS SQL Server (simple query).
- Basic read of the designated table (friends) created above
- Execute an aggregate SQL against the table
NOTE
It is possible to use transport.factory.instance or transport.instance they are the same. It allows the maintainers to know that we used a factory design pattern.
In [5]:
import transport from transport import providers import os AUTH_FOLDER = os.environ['DT_AUTH_FOLDER'] #-- location of the service key MSSQL_AUTH_FILE= os.sep.join([AUTH_FOLDER,'mssql.json']) msr = transport.instance(provider=providers.MSSQL,table='friends',auth_file=MSSQL_AUTH_FILE) _df = msr.read() _query = 'SELECT COUNT(*) _counts, AVG(age) from friends' _sdf = msr.read(sql=_query) print (_df) print ('\n--------- STATISTICS ------------\n') print (_sdf)
name age 0 James Bond 55 1 Steve Rogers 150 2 Steve Nyemba 44 --------- STATISTICS ------------ _counts 0 3 83
The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.
NOTE:
The auth_file is intended to be JSON formatted
In [3]:
{ "dataset":"demo","table":"friends","username":"<username>","password":"<password>" }
Out[3]:
{'dataset': 'demo', 'table': 'friends'}